"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Optional[int]    				=			(KDPMaDiscreteScheduler,)
				A__ :  str    				=			1_0

				def 						_a      ( self      ,					**_lowerCamelCase ):
										lowerCamelCase__   ={
										    "num_train_timesteps": 1100,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**_lowerCamelCase )
										return config

				def 						_a      ( self ):
										for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_lowerCamelCase )

				def 						_a      ( self ):
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]      ,					[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
																self.check_over_configs(beta_start=_lowerCamelCase      ,					beta_end=_lowerCamelCase )

				def 						_a      ( self ):
										for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=_lowerCamelCase )

				def 						_a      ( self ):
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =self.scheduler_classes[0]
										lowerCamelCase__   =self.get_scheduler_config(prediction_type="v_prediction" )
										lowerCamelCase__   =scheduler_class(**_lowerCamelCase )

										scheduler.set_timesteps(self.num_inference_steps )

										lowerCamelCase__   =self.dummy_model()
										lowerCamelCase__   =self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase__   =sample.to(_lowerCamelCase )

										for i, t in enumerate(scheduler.timesteps ):
																lowerCamelCase__   =scheduler.scale_model_input(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =model(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =scheduler.step(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																lowerCamelCase__   =output.prev_sample

										lowerCamelCase__   =torch.sum(torch.abs(_lowerCamelCase ) )
										lowerCamelCase__   =torch.mean(torch.abs(_lowerCamelCase ) )

										if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
																assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
										else:
																# CUDA
																assert abs(result_sum.item() - 4.693_4286_5017_0972E-07 ) < 1E-2
																assert abs(result_mean.item() - 0.0_0_0_2 ) < 1E-3

				def 						_a      ( self ):
										if torch_device == "mps":
																return
										lowerCamelCase__   =self.scheduler_classes[0]
										lowerCamelCase__   =self.get_scheduler_config()
										lowerCamelCase__   =scheduler_class(**_lowerCamelCase )

										scheduler.set_timesteps(self.num_inference_steps )

										lowerCamelCase__   =self.dummy_model()
										lowerCamelCase__   =self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase__   =sample.to(_lowerCamelCase )

										for i, t in enumerate(scheduler.timesteps ):
																lowerCamelCase__   =scheduler.scale_model_input(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =model(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =scheduler.step(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																lowerCamelCase__   =output.prev_sample

										lowerCamelCase__   =torch.sum(torch.abs(_lowerCamelCase ) )
										lowerCamelCase__   =torch.mean(torch.abs(_lowerCamelCase ) )

										if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
																assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3
										else:
																# CUDA
																assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
																assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3






				def 						_a      ( self ):
										if torch_device == "mps":
																return
										lowerCamelCase__   =self.scheduler_classes[0]
										lowerCamelCase__   =self.get_scheduler_config()
										lowerCamelCase__   =scheduler_class(**_lowerCamelCase )

										scheduler.set_timesteps(self.num_inference_steps      ,					device=_lowerCamelCase )

										lowerCamelCase__   =self.dummy_model()
										lowerCamelCase__   =self.dummy_sample_deter.to(_lowerCamelCase ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
																lowerCamelCase__   =scheduler.scale_model_input(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =model(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =scheduler.step(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																lowerCamelCase__   =output.prev_sample

										lowerCamelCase__   =torch.sum(torch.abs(_lowerCamelCase ) )
										lowerCamelCase__   =torch.mean(torch.abs(_lowerCamelCase ) )

										if str(_lowerCamelCase ).startswith("cpu" ):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
																assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3
										else:
																# CUDA
																assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
																assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3



"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				={
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a				=[
	    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MgpstrModel',
	    'MgpstrPreTrainedModel',
	    'MgpstrForSceneTextRecognition',
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	a				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


